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Nemcova A, Vargova E, Smisek R, Marsanova L, Smital L, Vitek M. Brno University of Technology Smartphone PPG Database (BUT PPG): Annotated Dataset for PPG Quality Assessment and Heart Rate Estimation. BIOMED RESEARCH INTERNATIONAL 2021; 2021:3453007. [PMID: 34532501 PMCID: PMC8440059 DOI: 10.1155/2021/3453007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 08/26/2021] [Indexed: 11/17/2022]
Abstract
To the best of our knowledge, there is no annotated database of PPG signals recorded by smartphone publicly available. This article introduces Brno University of Technology Smartphone PPG Database (BUT PPG) which is an original database created by the cardiology team at the Department of Biomedical Engineering, Brno University of Technology, for the purpose of evaluating photoplethysmographic (PPG) signal quality and estimation of heart rate (HR). The data comprises 48 10-second recordings of PPGs and associated electrocardiographic (ECG) signals used for determination of reference HR. The data were collected from 12 subjects (6 female, 6 male) aged between 21 and 61. PPG data were collected by smartphone Xiaomi Mi9 with sampling frequency of 30 Hz. Reference ECG signals were recorded using a mobile ECG recorder (Bittium Faros 360) with a sampling frequency of 1,000 Hz. Each PPG signal includes annotation of quality created manually by biomedical experts and reference HR. PPG signal quality is indicated binary: 1 indicates good quality for HR estimation, 0 indicates signals where HR cannot be detected reliably, and thus, these signals are unsuitable for further analysis. As the only available database containing PPG signals recorded by smartphone, BUT PPG is a unique tool for the development of smart, user-friendly, cheap, on-the-spot, self-home-monitoring of heart rate with the potential of widespread using.
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Affiliation(s)
- Andrea Nemcova
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, Brno 616 00, Czech Republic
| | - Enikö Vargova
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, Brno 616 00, Czech Republic
| | - Radovan Smisek
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, Brno 616 00, Czech Republic
- Institute of Scientific Instruments, The Czech Academy of Sciences, Kralovopolska 147, Brno 612 64, Czech Republic
| | - Lucie Marsanova
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, Brno 616 00, Czech Republic
| | - Lukas Smital
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, Brno 616 00, Czech Republic
| | - Martin Vitek
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, Brno 616 00, Czech Republic
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Nemcova A, Smisek R, Vitek M, Novakova M. Pathologies affect the performance of ECG signals compression. Sci Rep 2021; 11:10514. [PMID: 34006955 PMCID: PMC8131635 DOI: 10.1038/s41598-021-89817-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Accepted: 04/29/2021] [Indexed: 11/09/2022] Open
Abstract
The performance of ECG signals compression is influenced by many things. However, there is not a single study primarily focused on the possible effects of ECG pathologies on the performance of compression algorithms. This study evaluates whether the pathologies present in ECG signals affect the efficiency and quality of compression. Single-cycle fractal-based compression algorithm and compression algorithm based on combination of wavelet transform and set partitioning in hierarchical trees are used to compress 125 15-leads ECG signals from CSE database. Rhythm and morphology of these signals are newly annotated as physiological or pathological. The compression performance results are statistically evaluated. Using both compression algorithms, physiological signals are compressed with better quality than pathological signals according to 8 and 9 out of 12 quality metrics, respectively. Moreover, it was statistically proven that pathological signals were compressed with lower efficiency than physiological signals. Signals with physiological rhythm and physiological morphology were compressed with the best quality. The worst results reported the group of signals with pathological rhythm and pathological morphology. This study is the first one which deals with effects of ECG pathologies on the performance of compression algorithms. Signal-by-signal rhythm and morphology annotations (physiological/pathological) for the CSE database are newly published.
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Affiliation(s)
- Andrea Nemcova
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 12, 616 00, Brno, Czech Republic.
| | - Radovan Smisek
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 12, 616 00, Brno, Czech Republic.,Institute of Scientific Instruments, The Czech Academy of Sciences, Královopolská 147, 612 64, Brno, Czech Republic
| | - Martin Vitek
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 12, 616 00, Brno, Czech Republic
| | - Marie Novakova
- Department of Physiology, Faculty of Medicine, Masaryk University, Kamenice 753/5, 625 00, Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital Brno, Pekařská 53, 656 91, Brno, Czech Republic
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Maille B, Wilkin M, Million M, Rességuier N, Franceschi F, Koutbi-Franceschi L, Hourdain J, Martinez E, Zabern M, Gardella C, Tissot-Dupont H, Singh JP, Deharo JC, Fiorina L. Smartwatch Electrocardiogram and Artificial Intelligence for Assessing Cardiac-Rhythm Safety of Drug Therapy in the COVID-19 Pandemic. The QT-logs study. Int J Cardiol 2021; 331:333-339. [PMID: 33524462 PMCID: PMC7845555 DOI: 10.1016/j.ijcard.2021.01.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 12/17/2020] [Accepted: 01/07/2021] [Indexed: 12/23/2022]
Abstract
Background QTc interval monitoring, for the prevention of drug-induced arrhythmias is necessary, especially in the context of coronavirus disease 2019 (COVID-19). For the provision of widespread use, surrogates for 12‑lead ECG QTc assessment may be useful. This prospective observational study compared QTc duration assessed by artificial intelligence (AI-QTc) (Cardiologs®, Paris, France) on smartwatch single‑lead electrocardiograms (SW-ECGs) with those measured on 12‑lead ECGs, in patients with early stage COVID-19 treated with a hydroxychloroquine−azithromycin regimen. Methods Consecutive patients with COVID-19 who needed hydroxychloroquine−azithromycin therapy, received a smartwatch (Withings Move ECG®, Withings, France). At baseline, day-6 and day-10, a 12‑lead ECG was recorded, and a SW-ECG was transmitted thereafter. Throughout the drug regimen, a SW-ECG was transmitted every morning at rest. Agreement between manual QTc measurement on a 12‑lead ECG and AI-QTc on the corresponding SW-ECG was assessed by the Bland-Altman method. Results 85 patients (30 men, mean age 38.3 ± 12.2 years) were included in the study. Fair agreement between manual and AI-QTc values was observed, particularly at day-10, where the delay between the 12‑lead ECG and the SW-ECG was the shortest (−2.6 ± 64.7 min): 407 ± 26 ms on the 12‑lead ECG vs 407 ± 22 ms on SW-ECG, bias −1 ms, limits of agreement −46 ms to +45 ms; the difference between the two measures was <50 ms in 98.2% of patients. Conclusion In real-world epidemic conditions, AI-QTc duration measured by SW-ECG is in fair agreement with manual measurements on 12‑lead ECGs. Following further validation, AI-assisted SW-ECGs may be suitable for QTc interval monitoring. REGISTRATION:ClinicalTrial.govNCT04371744.
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Affiliation(s)
- Baptiste Maille
- Assistance Publique - Hôpitaux de Marseille, Centre Hospitalier Universitaire La Timone, Service de Cardiologie, Marseille, France; Aix Marseille University, C2VN, Marseille, France
| | - Marie Wilkin
- Assistance Publique - Hôpitaux de Marseille, Centre Hospitalier Universitaire La Timone, Service de Cardiologie, Marseille, France; Aix Marseille University, C2VN, Marseille, France
| | - Matthieu Million
- IHU-Méditerranée Infection, Marseille, France; Aix-Marseille University, IRD, APHM, MEPHI, Marseille, France
| | - Noémie Rességuier
- Department of Epidemiology and Health Economics, APHM, Marseille, France
| | - Frédéric Franceschi
- Assistance Publique - Hôpitaux de Marseille, Centre Hospitalier Universitaire La Timone, Service de Cardiologie, Marseille, France; Aix Marseille University, C2VN, Marseille, France
| | - Linda Koutbi-Franceschi
- Assistance Publique - Hôpitaux de Marseille, Centre Hospitalier Universitaire La Timone, Service de Cardiologie, Marseille, France; Aix Marseille University, C2VN, Marseille, France
| | - Jérôme Hourdain
- Assistance Publique - Hôpitaux de Marseille, Centre Hospitalier Universitaire La Timone, Service de Cardiologie, Marseille, France; Aix Marseille University, C2VN, Marseille, France
| | - Elisa Martinez
- Assistance Publique - Hôpitaux de Marseille, Centre Hospitalier Universitaire La Timone, Service de Cardiologie, Marseille, France; Aix Marseille University, C2VN, Marseille, France
| | - Maxime Zabern
- Assistance Publique - Hôpitaux de Marseille, Centre Hospitalier Universitaire La Timone, Service de Cardiologie, Marseille, France; Aix Marseille University, C2VN, Marseille, France
| | | | - Hervé Tissot-Dupont
- IHU-Méditerranée Infection, Marseille, France; Aix-Marseille University, IRD, APHM, MEPHI, Marseille, France
| | - Jagmeet P Singh
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jean-Claude Deharo
- Assistance Publique - Hôpitaux de Marseille, Centre Hospitalier Universitaire La Timone, Service de Cardiologie, Marseille, France; Aix Marseille University, C2VN, Marseille, France.
| | - Laurent Fiorina
- Institut Cardiovasculaire Paris-Sud, Hôpital Privé Jacques Cartier, Ramsay, Massy, France
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Nemcova A, Vitek M, Novakova M. Complex study on compression of ECG signals using novel single-cycle fractal-based algorithm and SPIHT. Sci Rep 2020; 10:15801. [PMID: 32978481 PMCID: PMC7519154 DOI: 10.1038/s41598-020-72656-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Accepted: 08/26/2020] [Indexed: 11/09/2022] Open
Abstract
Compression of ECG signal is essential especially in the area of signal transmission in telemedicine. There exist many compression algorithms which are described in various details, tested on various datasets and their performance is expressed by different ways. There is a lack of standardization in this area. This study points out these drawbacks and presents new compression algorithm which is properly described, tested and objectively compared with other authors. This study serves as an example how the standardization should look like. Single-cycle fractal-based (SCyF) compression algorithm is introduced and tested on 4 different databases-CSE database, MIT-BIH arrhythmia database, High-frequency signal and Brno University of Technology ECG quality database (BUT QDB). SCyF algorithm is always compared with well-known algorithm based on wavelet transform and set partitioning in hierarchical trees in terms of efficiency (2 methods) and quality/distortion of the signal after compression (12 methods). Detail analysis of the results is provided. The results of SCyF compression algorithm reach up to avL = 0.4460 bps and PRDN = 2.8236%.
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Affiliation(s)
- Andrea Nemcova
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 12, 616 00, Brno, Czech Republic.
| | - Martin Vitek
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 12, 616 00, Brno, Czech Republic
| | - Marie Novakova
- Department of Physiology, Faculty of Medicine, Masaryk University, Kamenice 753/5, 625 00, Brno, Czech Republic
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Smital L, Haider CR, Vitek M, Leinveber P, Jurak P, Nemcova A, Smisek R, Marsanova L, Provaznik I, Felton CL, Gilbert BK, Holmes Iii DR. Real-Time Quality Assessment of Long-Term ECG Signals Recorded by Wearables in Free-Living Conditions. IEEE Trans Biomed Eng 2020; 67:2721-2734. [PMID: 31995473 DOI: 10.1109/tbme.2020.2969719] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Nowadays, methods for ECG quality assessment are mostly designed to binary distinguish between good/bad quality of the whole signal. Such classification is not suitable to long-term data collected by wearable devices. In this paper, a novel approach to estimate long-term ECG signal quality is proposed. METHODS The real-time quality estimation is performed in a local time window by calculation of continuous signal-to-noise ratio (SNR) curve. The layout of the data quality segments is determined by analysis of SNR waveform. It is distinguished between three levels of ECG signal quality: signal suitable for full wave ECG analysis, signal suitable only for QRS detection, and signal unsuitable for further processing. RESULTS The SNR limits for reliable QRS detection and full ECG waveform analysis are 5 and 18 dB respectively. The method was developed and tested using synthetic data and validated on real data from wearable device. CONCLUSION The proposed solution is a robust, accurate and computationally efficient algorithm for annotation of ECG signal quality that will facilitate the subsequent tailored analysis of ECG signals recorded in free-living conditions. SIGNIFICANCE The field of long-term ECG signals self-monitoring by wearable devices is swiftly developing. The analysis of massive amount of collected data is time consuming. It is advantageous to characterize data quality in advance and thereby limit consequent analysis to useable signals.
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Maršánová L, Němcová A, Smíšek R, Vítek M, Smital L. Advanced P Wave Detection in Ecg Signals During Pathology: Evaluation in Different Arrhythmia Contexts. Sci Rep 2019; 9:19053. [PMID: 31836760 PMCID: PMC6911105 DOI: 10.1038/s41598-019-55323-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 11/25/2019] [Indexed: 11/09/2022] Open
Abstract
Reliable P wave detection is necessary for accurate and automatic electrocardiogram (ECG) analysis. Currently, methods for P wave detection in physiological conditions are well-described and efficient. However, methods for P wave detection during pathology are not generally found in the literature, or their performance is insufficient. This work introduces a novel method, based on a phasor transform, as well as innovative rules that improve P wave detection during pathology. These rules are based on the extraction of a heartbeats' morphological features and knowledge of heart manifestation during both physiological and pathological conditions. To properly evaluate the performance of the proposed algorithm in pathological conditions, a standard database with a sufficient number of reference P wave positions is needed. However, such a database did not exist. Thus, ECG experts annotated 12 chosen pathological records from the MIT-BIH Arrhythmia Database. These annotations are publicly available via Physionet. The algorithm performance was also validated using physiological records from the MIT-BIH Arrhythmia and QT databases. The results for physiological signals were Se = 98.42% and PP = 99.98%, which is comparable to other methods. For pathological signals, the proposed method reached Se = 96.40% and PP = 85.84%, which greatly outperforms other methods. This improvement represents a huge step towards fully automated analysis systems being respected by ECG experts. These systems are necessary, particularly in the area of long-term monitoring.
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Affiliation(s)
- Lucie Maršánová
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology Technická 12, Brno, 616 00, Czech Republic.
| | - Andrea Němcová
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology Technická 12, Brno, 616 00, Czech Republic
| | - Radovan Smíšek
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology Technická 12, Brno, 616 00, Czech Republic.,Institute of Scientific Instruments, The Czech Academy of Sciences Královopolská 147, Brno, 612 64, Czech Republic
| | - Martin Vítek
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology Technická 12, Brno, 616 00, Czech Republic
| | - Lukáš Smital
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology Technická 12, Brno, 616 00, Czech Republic
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A Comparative Analysis of Methods for Evaluation of ECG Signal Quality after Compression. BIOMED RESEARCH INTERNATIONAL 2018; 2018:1868519. [PMID: 30112363 PMCID: PMC6077674 DOI: 10.1155/2018/1868519] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Revised: 06/04/2018] [Accepted: 06/27/2018] [Indexed: 11/17/2022]
Abstract
The assessment of ECG signal quality after compression is an essential part of the compression process. Compression facilitates the signal archiving, speeds up signal transmission, and reduces the energy consumption. Conversely, lossy compression distorts the signals. Therefore, it is necessary to express the compression performance through both compression efficiency and signal quality. This paper provides an overview of objective algorithms for the assessment of both ECG signal quality after compression and compression efficiency. In this area, there is a lack of standardization, and there is no extensive review as such. 40 methods were tested in terms of their suitability for quality assessment. For this purpose, the whole CSE database was used. The tested signals were compressed using an algorithm based on SPIHT with varying efficiency. As a reference, compressed signals were manually assessed by two experts and classified into three quality groups. Owing to the experts' classification, we determined corresponding ranges of selected quality evaluation methods' values. The suitability of the methods for quality assessment was evaluated based on five criteria. For the assessment of ECG signal quality after compression, we recommend using a combination of these methods: PSim SDNN, QS, SNR1, MSE, PRDN1, MAX, STDERR, and WEDD SWT.
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Maršánová L, Ronzhina M, Smíšek R, Vítek M, Němcová A, Smital L, Nováková M. ECG features and methods for automatic classification of ventricular premature and ischemic heartbeats: A comprehensive experimental study. Sci Rep 2017; 7:11239. [PMID: 28894131 PMCID: PMC5593838 DOI: 10.1038/s41598-017-10942-6] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Accepted: 08/16/2017] [Indexed: 11/16/2022] Open
Abstract
Accurate detection of cardiac pathological events is an important part of electrocardiogram (ECG) evaluation and subsequent correct treatment of the patient. The paper introduces the results of a complex study, where various aspects of automatic classification of various heartbeat types have been addressed. Particularly, non-ischemic, ischemic (of two different grades) and subsequent ventricular premature beats were classified in this combination for the first time. ECGs recorded in rabbit isolated hearts under non-ischemic and ischemic conditions were used for analysis. Various morphological and spectral features (both commonly used and newly proposed) as well as classification models were tested on the same data set. It was found that: a) morphological features are generally more suitable than spectral ones; b) successful results (accuracy up to 98.3% and 96.2% for morphological and spectral features, respectively) can be achieved using features calculated without time-consuming delineation of QRS-T segment; c) use of reduced number of features (3 to 14 features) for model training allows achieving similar or even better performance as compared to the whole feature sets (10 to 29 features); d) k-nearest neighbours and support vector machine seem to be the most appropriate models (accuracy up to 98.6% and 93.5%, respectively).
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Affiliation(s)
- Lucie Maršánová
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 12, Brno, 616 00, Czech Republic.
| | - Marina Ronzhina
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 12, Brno, 616 00, Czech Republic
| | - Radovan Smíšek
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 12, Brno, 616 00, Czech Republic
- Institute of Scientific Instruments, The Czech Academy of Sciences, Královopolská 147, Brno, 612 64, Czech Republic
| | - Martin Vítek
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 12, Brno, 616 00, Czech Republic
| | - Andrea Němcová
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 12, Brno, 616 00, Czech Republic
| | - Lukas Smital
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 12, Brno, 616 00, Czech Republic
| | - Marie Nováková
- Department of Physiology, Faculty of Medicine, Masaryk University, Kamenice 5, Brno, 625 00, Czech Republic
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